System and method for generating a playlist from a mood gradient

ABSTRACT

Systems and methods for generating and playing a sequence of media objects based on a mood gradient are also disclosed. A mood gradient is a sequence of items, in which each item is media object having known characteristics or a representative set of characteristics of a media object, that is created or used by a user for a specific purpose. Given a mood gradient, one or more new media objects are selected for each item in the mood gradient based on the characteristics associated with that item. In this way, a sequence of new media objects is created but the sequence exhibits a similar variation in media object characteristics. The mood gradient may be presented to a user or created via a display illustrating a three-dimensional space in which each dimension corresponds to a different characteristic. The mood gradient may be represented as a path through the three-dimensional space and icons representing media objects are located within the three-dimensional space based on their characteristics.

BACKGROUND

With the emergence of digital media, that is audio, video (audiovisual)and text content stored digitally, users now have virtually unlimitedchoices. There is more media, in the form of electronic files andobjects, available to users now than they could be expected to consumein a lifetime. However, users can only experience media which they areable to find. Traditional media discovery involves searching for a trackby its title and/or creator, which information is often stored asmetadata in a media file. Yet for the majority of audio files on theweb, there is no such information or metadata. This is because thegeneration of metadata is a time consuming, typically manual task thatis often omitted by a media object's creator. There are millions of webmedia objects which are poorly annotated or lacking in any type ofmetadata whatsoever. These media will never be returned in any searchresult. There are also millions of “long tail” tracks which may beproperly annotated but are generally unknown and will therefore fall tothe bottom of any search query and, thus, for all intents and purposesrarely or never be selected by users.

SUMMARY

Systems and methods for presenting media information to users thatfosters discovery of new media and a media discovery interface aredisclosed. Media objects such as songs are analyzed to determine a setof three or more objective characteristics that describe the mediaobject. Icons representing the media objects are then presented to auser in display illustrating a three-dimensional space in which eachdimension corresponds to a different characteristic and the icons arelocated within the three-dimensional space based on theircharacteristics. In this way, media objects having similarcharacteristics are located near each other within the three-dimensionalspace allowing the user to see and access similar media objects quicklyand easily. Because the display does not rely on previously-generatedmetadata stored in a media file in a known format, the user may be shownmedia objects that have never been classified by users or which arerelatively unknown to the general population of users.

Systems and methods for generating and playing a sequence of mediaobjects based on a mood gradient are also disclosed. A mood gradient isa sequence of items which can be considered locations in n-dimensionalspace, in which each item is a representative set of characteristics ofa media object, that is created or used by a user for a specificpurpose. Given a mood gradient, one or more new media objects areselected for each item in the mood gradient based on the characteristicsassociated with that item. In this way, a sequence of new media objectsis created but the sequence exhibits a similar variation in media objectcharacteristics. The mood gradient may be presented to a user or createdvia a display illustrating a three-dimensional space in which eachdimension corresponds to a different characteristic. The mood gradientmay be represented as a path through the three-dimensional space andicons representing media objects are located within thethree-dimensional space based on their characteristics.

In one aspect, the disclosure describes a method for rendering to a usera playlist of songs, including a first song and a second song. Themethod includes receiving a user command to render the songs in theplaylist and playing a first song and a second song in the playlist viaa sound generation device. In response to receiving the user command torender, the method generates, based on an analysis of the songs in theplaylist, a first value for each of at least three objectivecharacteristics of the first song and a second value for each of the atleast three objective characteristics of the second song. The methodalso displays the first song as a first icon in a three-dimensionalspace of a user interface located at a first point in thethree-dimensional space based on three of the first values. In addition,the method includes displaying the second song as a second icon locatedat a second point in the three-dimensional space of the user interfacebased on three of the second values.

In another aspect, the disclosure describes a computer-readable mediumstoring computer executable instructions for a method of rendering mediaobjects. The method includes receiving a user command to render a firstmedia object and rendering the first media object to a user. In responseto receiving the user command to render, the method generates at leastthree values, each value representing a different objectivecharacteristic of the first media object determined based on an analysisof the first media object. The information identifying the first mediaobject and the generated values is then transmitted to a remotedatastore, whereby the remote datastore is populated with values for themedia objects rendered and analyzed by the rendering device or devicesperforming the method, such as client computing devices using localmedia players to render media objects.

The method may further include retrieving the media object from a remotelocation, wherein the information identifying the first media objectincludes an identification of the remote location. The method may alsoinclude generating a graphical user interface illustrating athree-dimensional space on a display device wherein each dimension ofthe three-dimensional space corresponds to a different objectivecharacteristic and displaying the first media object as a first icon inthe three-dimensional space located at a first point in thethree-dimensional space based on the generated value for the objectivecharacteristic corresponding to each dimension. The method may alsoinclude retrieving previously generated values for second media objectsand displaying each of the second media objects as second icons in thethree-dimensional space at different second points based on theretrieved values for the second media objects.

In another aspect, the disclosure describes a media rendering systemthat includes: a sound reproduction device that receives an electronicsignal and generates sound therefrom; a media player that renders mediaobjects to generate the electronic signal and passes the electronicsignal to the sound reproduction device; an analysis module thatanalyzes the media objects and, for each media object, generates a valuefor each of at least three objective characteristics; and a userinterface module that generates a three-dimensional user interface in adisplay device connected to the media rendering system, in which theuser interface is configured to represent a three-dimensional spaceincluding at least one first control representing a first media objectlocated in the three-dimensional space based on the values of three ofthe objective characteristics of the first media object.

The user interface module of the rendering system may further generate aplurality of second controls, in which each second control represents adifferent second media object located in the three-dimensional spacebased on values of three of the objective characteristics of thedifferent second media object. The media player may be furtherconfigured to retrieve media objects from a remote storage location andprovide the media objects to the analysis module in response to usercommands to render the media objects. The analysis module may alsotransmit the values generated to a remote datastore and requestpredetermined values for media objects from the remote datastore.

In another aspect, the disclosure describes a user interface stored asexecutable instructions in a memory of a computing system. The userinterface includes: a first display area illustrating athree-dimensional graphical space; and a plurality of controlsillustrated in the three-dimensional graphical space, for which eachcontrol corresponds to a different media object, each media object isdescribed by a different set of values, and each value is associatedwith a different objective characteristic of the media object. Inaddition, each dimension of the three-dimensional graphical spacecorresponds to a different objective characteristic and each locationwithin the three-dimensional graphical space corresponds to a differentset of values. Furthermore, each of the plurality of controls is locatedwithin the three-dimensional graphical space based on its associated setof values. Through the user interface, selection of a control causesthat selected control's corresponding media object to be rendered.

The user interface may further display a path illustrated through thethree-dimensional graphical space linking at least two of the pluralityof controls. If the media objects are songs, then at least one of theobjective characteristics may be selected from tempo, brightness,beatedness, spectral centroid, spectral rolloff, spectral flux, and zerocrossings. If the media objects are songs, then at least one of theplurality of controls may be derived from an image associated with thecorresponding media object. The user interface may be part of a mediaplayer or a rendering device that renders media objects including songs.

In another aspect, the disclosure describes a method of generating aplaylist of songs from a mood gradient. The method includes receiving arequest for a playlist of songs and retrieving a defined mood gradientcontaining a sequence of target value sets, in which each target valueset includes a plurality of values and each value represents a differentobjective musical characteristic. For each target value set in the moodgradient, the method selects a song based on the plurality of values inthe target value set. The playlist of songs is then generated, in whichthe playlist contains each selected song in the sequence of the moodgradient.

The method further may include identifying a user associated with therequest and selecting the defined mood gradient based on the userassociated with the request. In the method, retrieving a defined moodgradient may also include selecting the defined mood gradient based on amood gradient identifier contained in the request. Selecting a song mayinclude generating, based on audio data for each of a plurality ofsongs, a song value set associated with each song, in which each songvalue set includes a plurality of values—each value representing adifferent objective musical characteristic of the associated song, andcomparing the target value set to each of the song values sets. Themethod may include selecting the song having the song value set thatmost closely matches the target value set. The method may furtherinclude identifying a set of similar songs having song value sets withina predetermined variation of the target value set and randomly selectingthe song from the set of similar songs. Alternatively, the method mayinclude filtering the set of similar songs based on one or more filtercriteria and selecting the song from the filtered set of similar songs.

In another aspect, the disclosure describes a computer-readable mediumstoring computer executable instruction for a method of storing playlistdata. The method includes receiving a request to create a mood gradientand receiving a selection of songs in a sequence. In response, themethod includes generating, based on audio data for each song in thesequence, a target value set associated with each song, wherein eachtarget value set includes a plurality of values in which each valuerepresents a different objective musical characteristic of theassociated song. The target value sets and the sequence are then storedas the mood gradient.

In the method, the operation of receiving a selection of songs may alsoinclude displaying in a graphical user interface a plurality of songsincluding a first song and a second song as icons in a three-dimensionalspace. A first user selection of a first icon in the graphical userinterface representing the first song and a second user selection of asecond icon in the graphical user interface representing a second songmay be received. The method then may display a path through thethree-dimensional space between the first icon and the second icon.

The method may also include accessing a datastore of value sets fordifferent songs, searching the datastore for value sets for at least onesong in the sequence and retrieving the target value sets associatedwith the at least one song in the sequence. The method may also includestoring the mood gradient with information identifying a user associatedwith the request. The method may further include storing the moodgradient in location remote from the computer-readable medium.

In another aspect, the disclosure describes a system for generating aplaylist. The system includes an analysis module that analyzes songsand, for each song, generates a value set, wherein each value setincluding a value for each of at least three objective characteristicsof the associated song; a datastore containing plurality of value sets,in which each value set is associated with a different song; a userinterface module that receives a user selection of a mood gradient, inwhich the mood gradient identifies a group of objective characteristicvalue sets in a sequence; and a playlist generator module that selects arecommended song corresponding to each value set of the mood gradient.In the system, the playlist generator module may also generate aplaylist including the recommended songs corresponding to each of thegroup of objective characteristic value sets in the sequence.

The user interface module of the system may further generate athree-dimensional user interface in a display device connected to themedia rendering system, wherein the user interface is configured torepresent a three-dimensional space including at least one first iconrepresenting a first song located in the three-dimensional space basedon the values of three of the objective characteristics of the firstsong. The user interface module may further receive from the user afirst selection of songs in a sequence and a request to generate aplaylist containing a second selection of songs based on the firstselection of songs.

The system may further include a media player that renders songs inaccordance with commands received from a user, wherein the userinterface module provides an interface for the media player throughwhich the user may control the selection and rendering of songs to theuser.

The disclosed systems and methods enhance more traditional playbackexperiences. For instance, knowledge of the characteristics of the songsmay be used to find the smoothest path between all the tracks in a givenplaylist. This allows the system to automatically sort their records bya characteristic such as beats per minute and play a continuous streamof music at a consistent tempo, adjusting slightly from one song to thenext. Taking this one step further, auto-generated playlists may behumanized, taking a list of tracks that fall within a certain genre,were released in a specific year or are recommended for a specific userand then use tempo, beatedness, brightness and any other characteristicto intelligently sort the music. The result is a smoother and moreenjoyable music playback experience. An extension of this would be toallow the user to draw a tempo gradient on the screen, essentiallycorresponding to an evolving musical atmosphere which they would likecreated. The system can then generate a playlist of tracks filtered byany desired criteria and sorted in such a manner as to take the user ona “musical voyage”, as per their desired playlist mood curve. Differentmood curves could be used to deliver appropriate content based upon thespecific context of the activity the user is engaged in. One can imaginedifferent mood curves for working out, entertaining, romantic dinners,getting ready to go out or simply relaxing on a quiet Sunday afternoon.

Whereas current applications have been based around the concept of auser initiating a music playback experience and then sitting back andpassively listening to it, the disclosed systems extend this idea evenfurther to facilitate active music discovery experiences using engaginguser interface components. One instance of this would be a “genrediscovery” interface where users navigate between clusters of trackswithin a specific genre in three-dimensional space where the x, y and zaxes correspond to normalized values of tempo, beatedness and brightnessfor the given result set and different genres are represented bydifferent colored nodes in the space.

Another technique is to display audio search results in thisthree-dimensional space. This would facilitate a truly free and engagingsystem for discovering media that one would otherwise not be able toeasily find and categorize. The dimensions of this space could representtempo, beatedness and brightness or any other audio features that can becalculated. By simply changing a dimension from one audio feature to thenext the entire space may be reshaped, creating completely new resultsets. All those media tracks out there with no metadata, which wouldotherwise be relegated to the back end of an audio search result set andnever be seen, are now accessible through the novel interface whichmakes finding new music amusing entertainment in and of itself.

These and various other features as well as advantages will be apparentfrom a reading of the following detailed description and a review of theassociated drawings. Additional features are set forth in thedescription that follows and, in part, will be apparent from thedescription, or may be learned by practice of the described embodiments.The benefits and features will be realized and attained by the structureparticularly pointed out in the written description and claims hereof aswell as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application,are illustrative of embodiments systems and methods described below andare not meant to limit the scope of the disclosure in any manner, whichscope shall be based on the claims appended hereto.

FIG. 1 illustrates a high level embodiment of a system for generatingand displaying a media discovery interface to a user.

FIG. 2 illustrates another embodiment of a system for generating anddisplaying a media discovery interface to a user in a client-serverarchitecture.

FIG. 3 illustrates an embodiment of a method for rendering a song to auser utilizing the media discovery interface.

FIG. 4A illustrates an embodiment of a graphical user interface showingthe three-dimensional graphical space.

FIG. 4B illustrates another aspect showing an embodiment in which a pathis displayed on the interface.

FIG. 5 illustrates an embodiment of a simple method for generating aplaylist based on a seed song.

FIG. 6 illustrates an embodiment of a method for generating a playlistbased on a mood gradient.

FIG. 7 illustrates a graphical representation of the values for onecharacteristic of items in a mood gradient.

FIG. 8 illustrates yet another embodiment of a method of selecting songsbased on a mood gradient and displaying the playlist to the user in themedia discovery interface.

FIG. 9 illustrates an embodiment of a method for creating a moodgradient.

DETAILED DESCRIPTION

Systems and methods for presenting media information to users thatfosters discovery of new media and a media discovery interface aredisclosed herein. The content of the media is analyzed in order todetermine at least three different objective characteristics of themedia. Each of the items of media (media objects) are then displayed toa user, via a media discovery interface, as if they were inthree-dimensional space with each dimension corresponding to one of thecharacteristics. The media objects are located within the space based ontheir values for each of the characteristics. The media discoveryinterface allows users to visually see media objects that have similarcontent and thereby be alerted to the existence of media objects thatthe user may be interested in. Furthermore, the interface includesadditional novel functionality such as showing a path through thethree-dimensional space linking media objects that are to be played in aplaylist and allowing the user to select different characteristics foruse in displaying the media objects.

For the purposes of this disclosure, the term media object refers to anindividual item of media having characteristics that can be objectivelyanalyzed. A media object may be a true media object such as an audiofile containing a song in .mp3, format, .wav format or some other knownaudio format. A media object may also be a video file containing a videoclip or a movie in . mp4 or .avi format or some other video format.Depending on the embodiment, a media object may be media data streamedto the rendering device. Alternatively, the media object could be someother object of a known type such as a book or other physical product.In theory, the method could be adapted to display the similarity or makerecommendations based on any type of objects.

In this disclosure, however, for ease of reading embodiments of systemsand methods are described specifically with reference to media objectsthat are in the form of individual songs. One skilled in the art willrecognize that the systems and methods are not so limited and may beadapted to any media type including video and text media.

FIG. 1 illustrates a high level embodiment of a system for generatingand displaying a media discovery interface to a user. In the embodimentshown, a media rendering device 102 displays the media discoveryinterface 120 to the user, via a display 104. The discovery interface120 includes an area representing a three-dimensional space 124 and mayalso include one or more controls in one or more control areas 122, suchas a toolbar as shown. The media discovery interface 120 is discussed ingreater detail below.

The rendering device 102, through its display 104 and speaker 106, iscapable of rendering media objects 154 to a user. As mentioned above,the media objects 154 are in the form of songs 154 that can be played bythe rendering device to the user via the speaker 106. The songs 154 maybe stored in memory 162 or in a remote or attached datastore 164 thatmay or may not be internal to the rendering device 102.

The media rendering device 102 is provided with peripherals including adisplay 104 and speaker 106. These peripherals may be part of therendering device 102 or separate peripherals designed to be connected tothe rendering device 102 via communication ports or other electronicconnections as are known in the art. The display 104 may be an internaldisplay built into the rendering device for displaying information to auser. Alternatively, the display may be an external display, such as aflat screen or cathode ray tube monitor. The speaker 106 may be astandard speaker built into the device or external to the device.Alternatively, the speaker 106 may be a simple set of headphonesattached to the device or any other sound-generating device throughwhich audible sounds or music may be played to the user.

In FIG. 1 the rendering device 102 is illustrated as a computing devicehaving a processor 160 that executes software, such as the operatingsystem software 166 and media player application 108 as shown, stored onmemory 162 as is now common for many rendering devices, such as the RIOby Diamond, the IPOD NANO by Apple and the ZUNE by Microsoft. Inalternative embodiments the rendering device 102 may have differingcombinations of hardware and software or firmware, and furthermore maybe adapted for the type of media object to be rendered. For example, inone alternative embodiment the rendering device 102 may be a purposebuilt piece of hardware having little or no software. In anotherembodiment, the rendering device may be a general purpose computingdevice such as a personal computer.

As discussed, the rendering device 102 illustrated further includesoperating system software 166. Operating system software 166 is known inthe art and examples include WINDOWS software by Microsoft Corporation,as well as Linux-based operating systems, such as that provided by RedHat, Inc, Purpose-built operating system software 166 may also be usedas different embodiments of rendering devices 102 may require more orless functionality than that encountered on a personal computer.

A rendering device 102 may include one or more of a variety ofcomputer-readable media for storing the software, including the memory162 and datastore 164 shown. Computer-readable media can be anyavailable media (internal or external) that can be accessed by thedevice 102 and includes both volatile and nonvolatile media, andremovable and non-removable media. By way of example, and notlimitation, computer-readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatile,nonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canaccessed by rendering device 102.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The system memory 162 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) andrandom access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within rendering device 102, such as during start-up, istypically stored in ROM. RAM typically contains data and/or programmodules that are immediately accessible to and/or presently beingoperated on by processor 160.

In the computing device embodiment shown, the rendering device 102 isprovided with a media player application 108. The application 108 is asoftware application or a combination of software and hardware in whichthe software is executed by the processor 160 in order to render thesongs 154 to the user. Media player applications are known in the artand examples of media player applications include the Windows MediaPlayer by Microsoft, Apple's iTunes player, and Yahoo!'s Media Jukeboxto name only a few.

A media player application 108 as shown includes the software necessaryto generate and display a user interface 120 in the form of a the mediadiscovery interface 120 to the user via the display 104. In theembodiment shown, this is illustrated by the user interface module 114.The user interface module 114 generates the user interface 120 on thedisplay 104 and monitors the user interface 120 for interactions fromthe user (e.g., mouse clicks, control or icon 126 selections, or othercommands) and passes those commands (which may also be consideredrequests to perform an action) to processor 160 and/or media playerapplication 108.

The user interface module 114 includes the ability to generate andmanipulate the representation of the three-dimensional space 124 so thatdifferent views may be generated allowing the user to get theperspective of “lying through” the space 124. Thus, the user interfacemodule 114 can cause the space 124 to appear to rotate and move relativeto the view of the user. This manipulation of the space 124 may be inresponse to user commands received, such as through a set of controls(not shown) in a control toolbar 122. The interface 120 allows users totravel through a three-dimensional space which contains media objectsrepresented as two-dimensional or three-dimensional shapes or icons 126in that space 124.

Interfaces having three-dimensional space are well-known in the gamingindustry and there are many different means for controlling the displayof three-dimensional space known in the art. For example, in oneembodiment a combination mouse and keyboard input scheme may be used inwhich the mouse is used to determine direction of view and keystrokesare used for movement forward, backward and side to side relative to thedirection of view. Other methods for user control of the display ofthree-dimensional space 124 are known in the art and any suitablecontrol means may be used with the interface 120 described herein.

In the embodiment shown, in addition to controlling the display of thethree-dimensional space 124, the user can operate the rendering device102 and cause songs to be rendered by the device 102, such as throughthe control toolbar 122 on the user interface 120. The control toolbar122 includes one or more user selectable controls (not shown) throughwhich requests and commands may be given to the rendering device 102.Such controls are known in the art and may include the familiar controlsfor PLAY, SKIP, FAST FORWARD, REWIND, PAUSE and STOP.

As described above, the user interface 120 includes an area thatdisplays a three-dimensional space 124 to the user. Illustrated withinthis three-dimensional space 124, one or more icons 126 (two are shown)each representing songs 154 are displayed. The icons 126 are locatedwithin the three-dimensional space 124 based on the characteristics ofthe songs as determined by an objective analysis of the objectivemusical characteristics (“characteristics”) of the songs 154.

The characteristics of each song are determined through analysis of therenderable data of media object, in this embodiment by an objectiveanalysis of the characteristics of the renderable music data of thesongs 154. Thus, objective characteristics of a song, and the valuesdescribing those characteristics, are not and should not be consideredtraditional metadata, which as the term metadata is used herein refersto data describing the song or media object that cannot be derived froman analysis of the renderable data of that song or object. Traditionalmetadata is typically such data as title, author/artist, images, text orother data that for one reason or another the creator of the mediaobject wished to associate with the renderable media data. Thecharacteristics of a song or media object, on the other hand, may bedetermined directly from an analysis of the renderable media data whichallows the objective characteristics for any media object or song to bedetermined from that object, even in the complete absence of anymetadata or other information about the object.

In the embodiment shown, the rendering device 102 is provided with ananalysis module 110 for performing the analysis of the songs andidentifying the characteristics used for display on thethree-dimensional space 124. In an alternative embodiment, the analysismodule 110 is not provided on the rendering device 102, but thenecessary data describing the characteristics is either known (e.g., isstored on the computer-readable media of the rendering device 102 suchas with other metadata associated with each song) or available to therendering device 102, such as via a network from a remote datastore 164.

In an embodiment, the analysis module 110 has the ability to calculateaudio content characteristics of a song such as tempo, brightness orbeatedness, as described in greater detail below. Any objective audio orvideo characteristic may be evaluated and a representative valuedetermined by the analysis module 110. The results of the analysis are aset of values or other data that represent the content of a media object(e.g., the music of a song) broken down into a number ofcharacteristics. Such characteristics may include tempo, beatedness andbrightness, as discussed below. Other characteristics are also possible.In the embodiment shown, at least three characteristics of each mediaobject are analyzed. This allows each object to be represented inthree-dimensional space 124 on the media discovery interface 120 of thedisplay 104 as a location in that space. The characteristics for eachsong 154 may be stored locally and/or transmitted to a remote datastore.

The analysis may be performed automatically either upon each detectionof a new song 154 on the rendering device 102, or as each new song 154is rendered. The user may also be able to have input into thecalculation of the various characteristics analyzed by the analysismodule 110. The user can check to see what tempo has been calculatedautomatically and manually adjust these parameters if they believe thecomputer has made an error. The analysis module 110 may calculate thesecharacteristics in the background or may alert the user of thecalculation in order to obtain input from the user.

In an embodiment, the analysis module 110 is software executed by theprocessor 160. The software may be pre-existing as part of the mediaplayer application 108 or may be obtained on an as needed basis, asdescribed in greater with reference to the networked embodiment shown inFIG. 2. The analysis module 110 may be part of the media playerapplication 108, or independent modules as shown in FIG. 1.

In addition, the rendering device 102 may include a playlist generator112. In the embodiment shown, the playlist generator 112 is a softwaremodule that generates either a playlist (i.e. a list of songs which maybe in a sequence in which the songs are to be played) or sequentiallyselects songs, on an as needed basis, for rendering to the user. Theplaylist generator 112 may be part of the media player application 108,or an independent module as shown in FIG. 1.

In the embodiment shown, the playlist generator 112 selects songs basedon their characteristics as determined by the analysis of the musicdata. Given an initial song or set of values for differentcharacteristics (referred to as the “seed”), the playlist generator 112uses a similarity algorithm to identify one or more similar songs to theseed. The playlist generator 112 may select each song in the sequencebased only on its similarity to the song before it, or may select songsrelative to the seed only. The selection of similar songs based on knownvalues for objective characteristics is known in the art and anysuitable method may be used by the playlist generator 112.

The rendering device 102 illustrated also includes a mood gradient 170.The mood gradient 170 is a data structure that contains informationusable by the playlist generator 112 for the selection of songs for aplaylist. The mood gradient 170 contains a list of different sets ofobjective musical characteristics which may or may not be associatedwith the characteristics of a particular song. In an embodiment, eachset of characteristics may include a characteristic identifier (e.g.,“tempo”, “brightness”, or “tempo in beats per minute”) associated witheach value. In an alternative embodiment, additional information may beprovided to allow the playlist generator 112 to fully understand whatthe values in the sets of the mood gradient are. Such additionalinformation may include an identification of the method or algorithmthat produced the value and/or the units (e.g., beats per minute, beatsper second, etc.) for the values. This information in the mood gradientis then used to filter and order songs for a playlist. The mood gradient170 is discussed in greater detail below.

For example, in an embodiment discussed in greater detail below, theplaylist generator 112 is provided with mood gradient 170. Using this asa seed set of target values, the playlist generator selects one song foreach of the song/characteristic value set listed in the mood gradient170. The songs may be selected based on their similarity to thecharacteristics of each listing (i.e., each independent set ofcharacteristics) in the mood gradient 170. In this way, the playlistgenerator 112 can build a new playlist based on the mood gradient 170but that has dynamically selected songs, which can then be rendered tothe user, i.e., the songs in the new playlist rendered sequentially onthe speaker 106. Thus, in effect, a user can select a mood gradient 170and be presented with a dynamically-selected set of songs in which eachsong has similar characteristics to the characteristics in the moodgradient 170.

The use of the mood gradient 170 allows a user to have new and differentplaylists generated by the rendering device 102, but such playlists willstill conform to certain characteristics defined in the mood gradient170. This feature allows a user to generate new playlists for a givenmood. For example, a mood gradient 170 may be created for mellowlistening and another mood gradient 170 may be created for exercising.Yet another mood gradient 170 may be created for driving throughtraffic, air travel or driving on long, cross-country trips. In thisuse, a mood gradient 170 defines sets of characteristics and an orderingthat songs in a playlist should conform to.

A mood gradient 170 is illustrated as being stored in the memory 162 ofthe rendering device 102. A mood gradient 170 may also be stored indatastore 164 depending on the implementation of the system.

FIG. 2 illustrates another embodiment of a system for generating anddisplaying a media discovery interface to a user in a client-serverarchitecture. In the system 200 shown, a client computing device 202,which may be a rendering device 102 as discussed above with reference toFIG. 1, is shown attached to several server computing devices 230, 232,240 via a communication network 201. In the embodiment shown, thecommunication network 201 is the Internet 201, although anycommunication network may be used including a cellular telephonenetwork, wi-fi network, wide area network or a local area network.

FIG. 2 illustrates several of the components of an embodiment of aclient rendering device 202. The client rendering device 202 includes amedia player application 208 for rendering media objects 250, 252, 254,which operates as discussed above with reference to FIG. 1. The clientrendering device 202 is further illustrated as storing one or more mediaobjects in the form of songs 254 locally on the device 202. The locallystored songs 254 are distinguished from the songs stored remotely fromthe client rendering device 202, i.e., the songs 250 on server 230 andsongs 252 on server 232.

The client rendering device 202 is further illustrated as having ananalysis module 210. The analysis module 210, as discussed withreference to FIG. 1, analyzes songs 250, 252, 254 to determine a set ofvalues describing the characteristics of each of the songs analyzed. Inthe embodiment shown, these values for a song may be transmitted via theInternet 201 to a media server 240 for storage on a database 242 ofmedia object characteristic value sets. Media object characteristicvalue sets may also be stored on the client rendering device 202.

The client rendering device 202 further includes a playlist generator212 as described above with reference to FIG. 1. The client renderingdevice 202 is illustrated as being attached to a speaker 206 and adisplay 204. As described with reference to FIG. 1, the display 204illustrates a user interface 220 to the user. This user interfaceincludes a set 222 of controls, as well as a discovery interface area224, which displays a three-dimensional representation of each of thesongs in three-dimensional space based on their characteristics.

The system 200 illustrated includes servers 240, 230, 232 through whichthe client rendering device 202 may access remotely stored songs 250,252 and playlists 256, as shown. Such songs 250, 252 may be accessed bythe client rendering device 202 and rendered to the user via the speaker206. In the embodiment shown, when songs are accessed, the analysismodule 210 may generate the characteristics for each song as it isrendered. Alternatively, the analysis module 210 may retrieve previouslygenerated characteristics from a remote location such as the song/mediaobject characteristic database 242. Regardless of how thecharacteristics are obtained, the discovery interface area 224 willdisplay an icon, shape or some other visual representation 226 of theobject in the three-dimensional space 124 as described elsewhere. Assongs are rendered, the interface 220 may also show a path between songsproviding a visual representation through a three-dimensionalcharacteristic space of the path of objects that have been recentlyrendered.

In an embodiment, song characteristics (e.g., the set of values derivedfrom the analysis of the characteristics of the song) may be stored in aremote database 242 or locally on the client rendering device 202. Inyet another embodiment, such information may be stored with some or allof songs 250, 252, 254 such as metadata for each song stored within thecontainer file for song. Previously generated song characteristic valuesets are useful, as described in greater detail below, when generatingplaylists from a mood gradient or when displaying in three-dimensionalcharacteristic space songs accessible to the client rendering device 202but that may not have been rendered previously by the user.

For example, a user may wish to investigate a library of songs availableon a given server 230. Upon directing the user's client device 202 todisplay the songs 250 stored on the server 230, the user may bepresented with the three-dimensional space 224 illustrating some or allof the songs 250 as shapes 226 in the space 224. Based on the relativeproximity of the shapes 226 within the space 224, the user can identifywhich songs 250 are similar. The user may also be able to selectparticular shapes 226 to obtain more information, such as metadatadescribing the artist, song title, etc., in a separate pop-up window,tool tips window or on the shape 226 itself. The user may further beable to render some or all of the song by selecting the shape 226 fromthe interface 220.

FIG. 3 illustrates an embodiment of a method for rendering a song to auser utilizing the media discovery interface. The method begins with areceive request operation 302. In the receive request operation 302, auser has made a request or entered a command to play a song and thatrequest has been received by the rendering device. The request may bemade by the user selecting an icon, text string or shape associated witha song through a graphical user interface (e.g., double-clicking thetitle of the song) or issuing some command through the interface to playthe song. Alternatively, the user may have clicked on a link, causingthe rendering device to attempt to retrieve the song associated with thelink using the address information contained therein. Regardless of howthe request is initiated, it is received in the receive requestoperation 302.

In response to receiving the request, the rendering device retrieves therequested song in a retrieve song operation 304. This may require thesong to be accessed in local memory or retrieved from a remote storagedevice based on information contained in the request.

In the embodiment shown, after being retrieved the song is played in arender operation 306. The render operation 306 may include decompressingthe data of the song and converting it into some format, and thenpassing the information necessary to the speaker or other soundgeneration device in order to have the audio generated to the user.

In addition, method 300 includes an analysis operation 308 in which thesong is analyzed to determine the characteristics of the song. In theembodiment shown, a value is generated for each of a set of selectedcharacteristics that describes that characteristic. Such characteristicsmay be things such as tempo, beatedness, brightness, and other audiofeatures that can be analyzed mathematically based on the data withinthe song.

In the analysis operation 308, audio content analysis using digitalsignal processing techniques may be used to infer definitive musicalcharacteristics such as tempo, “beatedness” and brightness. Thesecharacteristics are then used to classify media, allowing songs whichwould otherwise have no metadata to be classified and displayed to auser via the media discovery interface. As described above, the analysismay be performed on a client or a server and thus the analysis may bedistributed throughout a network and need not be performed at the timeof display, as long as the values of the characteristics are availableto the displaying device. For example, the analysis operation 308 may beperformed on individual client machines as the user is consuming themedia via a web based media player.

The analysis operation 308 may use any and all types of audio contentfeatures known including compound features such as tempo or beatedness,lower level acoustic properties such as spectral centroid, spectralrolloff, spectral flux, zero crossings and also mel-frequency cepstralcoefficient (MFCC) features in order to classify media which wouldotherwise have no metadata. For example, in an embodiment threecharacteristics may be analyzed to obtains a set of values describingeach song—tempo, beatedness and brightness. An example of a softwaresystem and method for calculating characteristics for songs can be foundin the reference Tzanetakis et al. “MARSYAS: A Framework for AudioAnalysis” Organized Sound (1999), 4: 169-175 Cambridge University Press,which is hereby incorporated herein by reference.

Tempo represents the speed or pace of a piece of music. It is a usefuldescriptor and has an immediate and direct influence on the mood oratmosphere created by a piece of music. Tempo is one of the more commonattributes used for classifying music.

Beatedness represents the strength of the beat in the audio signal andbrightness is a measure of the overall timbre. Beatedness has a strongimpact upon the perceived tempo and the corresponding mood of a track.The stronger the beatedness, the more energy it tends to convey, withusers often believing a track with more beatedness is actually “faster”than a less beat oriented track with the identical tempo. Beatedness mayalso be used as an indicator of whether a video has a musical soundtrack(e.g., a music video) or not, or whether an audio file is a podcast withmostly speaking or a music track. Depending on the implementation, tempomay be used as the dominant characteristic, followed to a lesser degreeby beatedness and finally brightness which can be used to fine tune theresult space.

For renderable music data, many objective characteristics, which maysometimes also be referred to as features, are known in the art (forexample, see E. Scheirer & M. Slaney, Construction And Evaluation Of ARobust Multifeature Speech/music Discriminator, Proc. ICASSP-97, Munich(1997) which reference is hereby incorporated herein by reference) andany characteristics or set of characteristics may be used herein.Examples of other characteristics which may also be used include:

-   -   Spectral Centroid: The center-of-gravity of the magnitude        spectrum—A measure of the brightness of the sound.    -   Spectral Rolloff: The frequency in the magnitude spectrogram for        which 85% of the energy falls below. This is another measure of        the timbre of the sound.    -   Spectral Flux: The amount of change in the spectrum between        frames. This is computed by squaring the difference between        successive spectrogram frames.    -   Zero Crossings: The number of sign changes in the acoustic        waveform over a window. This is a measure of the dominant        frequency in the signal.

For each of these four basic characteristics, four different statisticsmay be calculated. They are as follows:

-   -   The mean of the mean: Calculate the mean over 40 frames, and        then calculate the mean of this statistics. This is equivalent        to a single calculation of the mean over the entire 30 seconds.    -   The mean of the standard deviation: Calculate the standard        deviation of the audio feature over 40 frames, and then        calculate the mean of these standard deviations over the entire        30 seconds. This reflects how the music changes over small        windows of time.    -   The standard deviation of the mean: Calculate the mean of the        feature over 40 frames, and then calculate the standard        deviation of the feature. The 40-frame window size gives a        reliable measure of the feature over a short window, which can        then be compared to other windows to understand how it changes        over time.    -   The standard deviation of the standard deviation: Calculate the        standard deviation of the feature over 40 frames, and then        calculate the standard deviation of this measure over the        seconds.

In addition there are many characteristics that measure the rhythmiccontent of the music using a beat histogram calculated by measuring thetemporal correlation of the energy in the signal over windows of up to1.5 seconds. The first two peaks are identified in this beat histogramand their properties are captured as features. The rhythmiccharacteristics include:

-   -   High Peak Amplitude: the size of the biggest peak in the beat        histogram.    -   High Peak Beats-per-minute: the speed of the primary (or        loudest) beat.    -   Low Peak Amplitude: the size of the second-biggest peak in the        peak histogram.    -   Low Peak Beats-per-minute: the speed of the second-loudest beat.    -   Peak Ratio: Ratio of the amplitude of the second peak to the        amplitude of the first.    -   Three features based on energy measures.

The above is just one list of exemplary characteristics that may beanalyzed in order to classify a song. Other characteristics are alsopossible. In addition, many other characteristics that are related tovideo or other aspects of content may be used when analyzing differenttypes of media objects, such as media objects containing video.

As described above, in an alternative embodiment the analysis operation308 may occur independently of the rendering operation 306. For example,whenever a device is alerted to a new media object or song, the analysismodule may analyze the song and transmit the characteristics to adatastore for storage. In this way, each device may become a retrievalmechanism that helps populate a central remote datastore with value setsfor songs. In this embodiment, the analysis need be performed only onceand for all subsequent instances in which the song's characteristics areneeded the system retrieves them from the datastore.

After the analysis operation 308, a transmission operation 310 transmitsthe characteristics determined by the analysis operation to one or moredatastores. In the method 300 shown, a system such as that shown in FIG.2 is provided and the transmission operation 310 transmits thecharacteristics of the song to the remote datastore 242 of a server.This information can then be retrieved and further analyzed at a latertime by other client devices.

The method 300 also includes generating the three-dimensional graphicalspace in the user interface in a generate interface operation 312. Inthe generate interface operation 312, in addition to controls and othermeans for interacting with the media player and/or the rendering device,the representation in a three-dimensional space is created and displayedto the user.

After the three-dimensional space has been generated, it is populated ina display icons operation 314. In this operation 314, thecharacteristics of the songs to be displayed (which may be all of thesongs known to the device or some subset of songs) are identified and,for each of the songs to be displayed in the interface, an icon isgenerated.

The icon, which may be a simple three-dimensional object or some moreelaborate object such as a representation of a vinyl record coverfloating in space at various angles is placed in a location within thethree-dimensional space depending on the characteristics calculated forthe associated song. Thus, some icons will appear farther away or closerdepending on the relative characteristics of each song. Furthermore,songs with similar characteristics will appear grouped together or inclose proximity relative to songs with less or more characteristics.

The icons generated as part of the display icon operation 314 mayinclude images or artwork associated with each of the songs for each ofthe icons. For example, in an embodiment already mentioned above, eachicon may be a three-dimensional image or a three-dimensionalrepresentation of an album with the original album art floating in spaceat some angle where the center of the album is located at the point thatcorresponds to the characteristics of that album.

As part of the display icons operation 314, a determination is made asto what songs will be displayed and data for those songs is retrieved inorder for the icons for those songs to be located in the appropriateplace within the three-dimensional space. This may include retrievingcharacteristics already generated by the rendering device or retrievingcharacteristics previously generated by a different device and storedeither on the rendering device or on a remote web server.

The method 300 may be repeated each time a song is played by a user. Forexample, a user may select a playlist, thus generating a series ofreceive request operations 302 as the rendering device plays each songin turn, each song of the playlist being considered a separate request,each song being analyzed in an analysis operation, and thecharacteristics subsequently transmitted in a transmission operation 310to the remote data store. The analysis operation 308 and transmissionoperation 310 may be performed at the time the song is played.Alternatively, the operations may be performed at the time the playlistrequest is received. Thus, for example, all of the songs on the playlistmay be presented in the three-dimensional graphical space substantiallyimmediately after receiving the command to play the playlist, if therendering device is fast enough to analyze all of the songs or canretrieve the characteristics of all of the songs quickly. Thus, eachsong will be displayed in the three-dimensional graphical space.

The method 300 may also be used to display similar songs to the user. Inthis embodiment, as the method 300 is performed, the display iconsoperation 314 may include retrieving characteristics of a plurality ofsongs and displaying all of the songs as icons in the three-dimensionalspace. Thus, the user is made aware and able to select any of the iconsto either instantly play another song, to select a next song, or toselect the selected song as the next song to play after playing thecurrent song.

The songs displayed in the display icons operation 314 may be locallystored songs or songs accessible from a remote source such as a mediaserver. In this embodiment, the display icons operation 314 may includeretrieving one or more sets of values of songs that are not locallystored or values of songs that are locally stored but in which the songsthemselves reside on a remote data store.

The display icons operation 314 as mentioned above may includeretrieving an image associated with each song such as album cover art.In addition, it may include retrieving differently shaped icons torepresent different pieces of information to the user. For example, anicon may be represented as a sphere if it represents a song in aspecific genre such as jazz, and may be represented as a cube if it isfor a song in another genre such as classical.

In addition, the display operation 314 may include a filtering operationin which only songs of a certain genre having a certain average userrating or higher, or other criteria, may be used to screen the songsthat are to be displayed on the display generated in the generateinterface operation 312. If more than one song is played, or if aplaylist has been selected, a path between songs may be illustrated inthe interface, such as the one shown in FIG. 4B. This may be performedin a display path operation (not shown) in which, after the icons havebeen populated into the three-dimensional interface, a path of some kindis drawn between objects, such as between the songs of the playlist.

The interface itself may be redrawn in response to user commands or asdifferent songs are played so that the current song is at a specifiedpoint within the three-dimensional graphical space. Alternatively, thesongs could be colored or shown to be blinking or given some other typeof emphasis as its corresponding song is playing to indicate that it isthe song that is currently playing. Thus, for each icon illustrated inthe three-dimensional space, there may be a plurality of differenticons, one with emphasis, one with out, one showing the icon as beingthe next to play, or one showing the icon as having played in the past.Thus, each icon may, in fact, be a set of icons for use in differentsituations.

For example, in a first embodiment, the display icons operation 314 maycenter the three-dimensional space on a first icon while the first songis playing. As a next song is playing, a path may be drawn from thefirst icon to the second icon, and the interface redrawn to illustratemovement from the first to the second icon in centering thethree-dimensional space on the second icon. The view as shown with theother objects may be modified as well to view from a different angle sothat the full path may always be displayed to the user, allowing theuser to see all of the songs on the playlist represented by the path.

As mentioned above, in the generate interface operation 312, athree-dimensional space is generated having axes corresponding to threepreviously selected characteristics, e.g., tempo, beatedness andbrightness. Each characteristic of the displayed characteristics may beuser-selectable. For example, a user may, through the interface, selectfrom a set of objective characteristics to be displayed as dimensionswithin the interface. This selection may be received in a receive userselection operation (not shown) which may occur at any time during orbefore the method 300 to allow the user to set up the interface inresponse to the user's preferences. This information is then used whengenerating the three-dimensional space in the interface. In addition,the user may select such things as scale, thus making objects closertogether or farther away for a given characteristic. In addition, theXYZ coordinates may be dictated so that the user can have a specifiedcharacteristic in a specific dimension.

The space that the user finds themselves navigating within is theacoustic similarity space associated with the seed song based upon theset of acoustic features or characteristics used for parameters x, y andz. If the user navigates beyond a threshold, for example 20% away fromthe seed song, then the space will be recalculated based upon thenearest song. The user may also choose to alter the dimensions whichmake up the space they are in as an alternative navigation mechanism.For example, they could choose to switch the X dimension from tempo tospectral rolloff or zerocrossing. At this point a new space would becalculated and redrawn based upon the nearest media object as the seedtrack and the new X, Y and Z acoustic feature parameters.

FIG. 4A illustrates an embodiment of a graphical user interface showingthe three-dimensional graphical space. FIG. 4A illustrates an embodimentof a graphical user interface 20 having a plurality of windows, eachproviding various functionality to the user, including an embodiment ofa three-dimensional space as described herein. In an embodiment, theinterface may be generated by a media player application or by a browseraccessing a web page with an embedded media player application.

The user interface 20 may include a first parent window 30 thattypically defines the general size, color, and layout of the interfaceand includes window control buttons 28 (e.g., minimize, close, etc.) forthat window 30. The interface 20 may also comprise a second parentwindow 36 (a child to the first parent window) within the first parentwindow 30, and one or more child windows 38 dependent from the secondparent window 36. The second parent window 36 and child windows 38typically define information and/or functionality that will assist auser when accessing and navigating the features of the player.

For example, the second parent window 36 and its child windows 38 mayprovide toolbars, pull-down menus, Plug-ins, applications, etc. Forexample, two child windows 38 provided at the top (in the drawing) ofthe interface 20 define two toolbars 22, which may include a variety ofinterface controls 24 such as, for example, pull-down menus, functionalbuttons (e.g., stop, back, forward, home, etc.), and a combination offunctional buttons and windows (e.g., a search button and window). Theuppermost toolbar 22 provides a plurality of pull-down menus 24 and thelower toolbar 22 provides a plurality of functional buttons 24. In anembodiment, the user may toggle any of the (in the drawing) toolbars 22on and off using a View toolbar control (pull-down menu) provided in theupper toolbar 22.

A content window 32 is also provided as part of the interface 20 withinwhich three-dimensional space is displayed. The three-dimensional spaceis illustrated in the lower window 32 of the interface 20. In this area,the three-dimensional graphical space is essentially represented by anempty space populated with a number of icons; in this case, in the formof rectangular prisms shown in three dimensions in a perspective view.The prisms have a different size and orientation in the embodimentshown, in order to give the user visual cues concerning their relativespacing in the three-dimensional space. For example, the size is used toillustrate how near or far away they are in the three-dimensional spacerelative to the user's viewpoint. Thus, smaller icons 40 illustrate thatthe song is farther away, in terms of its characteristics, from theviewer than a larger icon 40. Although not shown, other items may beincluded in the three-dimensional space, such as visual axes, scales andbackground, in order to provide the user with more context of the threedimensions of the space.

In the embodiment shown, in FIG. 4A, one of the icons 40 is displayedwith a different fill pattern than the other icons 40. This icon 42 isused to illustrate that the song corresponding to the icon 42 iscurrently being rendered. In addition to shapes, icons 40 may haveartwork, as discussed above, such as album cover art, text or otherinformation displayed on the surface of the icon. For example, icons maybe illustrated by spheres covered with the album art or may beillustrated by cubes of text or cubes of photos of the artist and/orname of the album.

In an embodiment, users are able to navigate through the space shown inthe content window 32 using the directional keys of their keyboard orsimply by moving their mouse forward and back or left and right. Theycan pivot in the space by holding down the Ctrl key or mouse wheel andsliding the mouse left or right. Different types of media (for exampleaudio or video) can be represented by differently shaped icons 40. Anyavailable album art may be projected onto the icon 40 and any availablemetadata appears when the user rolls over the shape with their mouse. Inan embodiment, each icon 40 is also a user selectable control such that,when the user clicks on the icon 40, the song is launched (for examplein a separate media player) and rendered. The three-dimensional spacemay then be recalculated with the location of the selected icon 40 asits centroid. Additional song characteristics (or metadata) can berepresented using the size and color of the shapes which represent songsin this space.

The icons 40 generated on the user interface may also be referred to ascontrols. The icons 40 may be user-selectable in order to cause the songassociated with a selected icon 40 to be rendered to the user. The usermay select the icon 40 by means of a pointing device such as a mouse andby clicking or double-clicking upon an icon 40. Alternatively, an icon40 may be selected through user inputs in a touchpad or arrow pad,allowing the user to select different icons 40 and issue commandscausing the songs associated with those icons 40 to be rendered by therendering device.

FIG. 4B illustrates another aspect showing an embodiment in which aplaylist or mood gradient as discussed later, is displayed on theinterface. In addition to the icons 40 and the currently rendered icon42, a path 50 is illustrated between four icons in this example. Thepath 50 begins at a first icon 44, travels through the currentlyrendered icon 42, to a second icon 46 and yet another icon 48. In theembodiment shown, the path 50 is illustrated by a dashed black line 50.Any means of illustrating the path 50 may be used from a dashed line toa solid line to a straight line to lines with a smooth curve (such asshown) to some other effect illustrating the songs as being linked insequence. As discussed above, the view of the three-dimensional spacemay be altered by the system so that all points on the path 50 areinitially displayed to the user until such time as the user takescontrol of the display and causes the view to change.

FIG. 5 illustrates an embodiment of a method for generating a playlistbased on a seed song. In the method 500, a request is received for aplaylist based on a seed song in a receive request operation 502. Therequest may be a request for a playlist, i.e., a list of songs in asequence. Alternatively, it may be a request for a rendering device torender a series of songs starting from the seed song or a song similarto the seed song until a cease command is given by the user. What isultimately generated by the method 500 will be dictated on what therequest is for, i.e., a simple list in text, a group of songs, or songsto be rendered in series to the user.

After the request is received, the values for the characteristics of theseed song are obtained in an obtain by a set operation 504. This mayrequire that the seed song be evaluated by the rendering device in orderto obtain the value set describing the song's characteristics.Alternatively, the value set of the characteristics of the seed song maybe retrieved from a local or remote storage based on identificationinformation of the seed song included in the request.

Regardless of how the value set is obtained, a song is then selectedbased on the seed song's value set of characteristics in a selectionoperation 506. Such a selection may be a simple ranking to identifysongs with the closest value set to that of the seed song.Alternatively, filtering may also be done in order to filter out some ofthe potential songs that may be selected, i.e., filtering out or inspecific genres, or songs by specific artists. More complicatedselection algorithms may also be used: algorithms based on weightingeach of the different characteristics differently, algorithms based onpast user selections, and algorithms based on known user tastes.

Regardless of how the selection is performed, after the first selectionoperation 506, a selected song is added to the playlist in an add songoperation 508. Next, a determination operation 510 determines if theplaylist is complete (e.g., the playlist has the requested number ofsongs in it, etc.). For example, if only one song is required, i.e., therequest is for a song similar to the seed song, then the playlist iscomplete and the method 500 terminates as shown.

The determination operation 510 includes returning to the requestor theinformation in the format required. This may include transmitting thesong to the rendering device or playing the song on the renderingdevice. In addition, or alternatively, it may include transmitting textor displaying text to the user identifying the selected song. In yetanother embodiment, it may include transmitting a playlist to the user,the playlist containing information necessary for the rendering deviceto access the song or songs of the playlist at a later time.

If the playlist is determined not to be complete by the determinationoperation 510, then a select next song operation 512 is performed. Inthe select next song operation 512, the next song is selected based oneither the seed song's value set or based on the value set ofcharacteristics for the previously selected song. The method ofselection may be determined by default or by a user command contained inthe request or set prior to submitting the request. Thus, the user mayrequest a playlist of songs in which every song is similar to the seedsong. Alternatively, the user may also request a playlist of songs inwhich each song is similar to the song before it but not necessarilysimilar to the seed song, thus allowing the nature of the songs in theplaylist to evolve over time.

After the select next song operation 512, the selected song is added tothe playlist in the addition operation 508 and the determinationoperation 510 is repeated again. In this way, a playlist may be builtuntil a playlist of the appropriate size is generated. In an embodiment,the determination operation 510 may not be an operation that looks for aspecific number of songs, but rather repeats until the user issues acessation command. In this way, the method 500 can be used to generate asequence of songs that can be rendered to the user one after the nextuntil the user issues a command to stop rendering songs.

For example, in an embodiment a request is received for a playlist of100 media objects which are “similar” to a seed track. Similarity iscalculated by finding the seed track's nearest neighbors in the chosenacoustic feature set. The results are returned in the form of an XMLdocument.

The following are examples of documents that could be part of a systemfor generating a playlist from a mood gradient. Document 1 is a playlistcorresponding to a particular set of songs. The set may be all the songsin a library, all the songs available to the system for rendering, or asubset of songs that the user identified to be used when generatingplaylists. Document 2 is a mood gradient and Document 3 is a playlistdocument generated by applying the mood gradient to the characteristicvalue sets of the songs contained in Document 1. The songs in theDocument 3 are selected from the set of songs identified in Document 1.The example documents are written in XML but any language or data formatmay be used as long as the rendering device and interface can interpretthe information contained therein. The mood gradient and generatedplaylist are based upon three characteristics identified as x, y, and z,which for example, could be the acoustic features tempo, brightness and“beatedness”.

Document 1) Playlist of Songs Available

<?xml version=“1.0” encoding=“UTF-8”?> <playlist version=“1”xmlns=“http://xspf.org/ns/0/”>  <title>music from scissorkick.com andaurgasm.us</title>  <creator>Yahoo! Playthispage</creator>  <trackList> <track>  <location>http://aurgasm.us/music/julija/Petra%20Jean%20Phillipson%20-%20Play%20Play.mp3</location>   <title>Petra Jean Phillipson - PlayPlay</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Petra%20Jean%20Phillipson%20-%20I%20Want%20The%20Impossible.mp3</location>   <title>Petra JeanPhillipson - I Want The Impossible</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Federico%20Aubele%20-%20Ante%20Tus%20Ojos.mp3</location>   <title>Federico Aubele - Ante TusOjos</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Federico%20Aubele%20-%20En%20El%20Desierto.mp3</location>   <title>Federico Aubele - En ElDesierto</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Tony%20Allen%20-%20Crazy%20Afrobeat.mp3</location>  <title>Tony Allen - Crazy Afrobeat</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Tony%20Allen%20-%20Every%20Season.mp3</location>  <title>Tony Allen - Every Season (feat</title>  </track>  <track>  <location>http://aurgasm.us/music/Silent%20Land%20Time%20Machine%20-%20The%20Thing%20This%20Doesnt%20Mean%20Is%20Nothing.mp3</location>  <title>Silent Land Time Machine - The Thing This Doesn't Mean IsNothing</title>  </track>  <track>  <location>http://aurgasm.us/music/Silent%20Land%20Time%20Machine%20-%20Everything%20Goes%20To%20Shit.mp3</location>   <title>Silent LandTime Machine - Everything Goes To Shit</title>  </track>  <track>  <location>http://aurgasm.us/music/Priscilla%20Ahn%20-%20Dream.mp3</location>  <title>Priscilla Ahn - Dream</title>  </track>  <track>  <location>http://aurgasm.us/music/Priscilla%20Ahn%20-%20Lullaby.mp3</location>  <title>Priscilla Ahn - Lullaby</title>  </track>  <track>  <location>http://pharrellfluokids.free.fr/Hafdis%20Huld%20-%20Younger%20longer.mp3</location>  <title>Younger Longer</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Hafdis%20Huld%20-%20Tomoko.mp3</location>  <title>Hafdis Huld - Tomoko</title>  </track>  <track>  <location>http://aurgasm.us/music/julija/Hafdis%20Huld%20-%20Diamonds%20On%20My%20Belly.mp3</location>   <title>Hafdis Huld -Diamonds On My Belly</title>  </track>  <track>  <location>http://aurgasm.us/music/Marsen%20Jules%20-%20Coeur%20Saignant.mp3</location>  <title>Marsen Jules - Coeur Saignant</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/02%20Clocks.mp3</location>  <title>02 Clocks</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/cinematic_asthestarsfall.mp3</location>  <title>cinematic asthestarsfall</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/01%20Sneaky%20Red.mp3</location>  <title>01 Sneaky Red</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/Nostalgia.mp3</location>  <title>Nostalgia</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/Maserati_synchronicity.mp3</location>  <title>Maserati synchronicity</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/ben_sideways_cerebus.mp3</location>  <title>ben sideways cerebus</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/ben_selectiveperiphera.mp3</location>  <title>ben selectiveperiphera</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/02%20Beats%20Mistake.mp3</location>  <title>02 Beats Mistake</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/dragons_your_way_too.mp3</location>  <title>dragons your way too</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/Ticklah_TwoFace.mp3</location>  <title>Ticklah TwoFace</title>  </track>  <track>  <location>http://www.scissorkick.com/blog/music/People_Staying_Awake.mp3</location>  <title>People Staying Awake</title>  </track>  </trackList></playlist>Document 2) Example Mood Gradient

<?xml version=“1.0” encoding=“UTF-8”?> <moodgradient version=“1”xmlns=“http://research.yahoo.com/moodgradient/ns/0/”>  <title>My SampleGradient - Always gets me through the day.</title>  <author>WilliamWhite</author>  <created>2007-08-22T06:33:11Z</created> <characteristics>  <characteristic dimension=“x”>Tempo</characteristic> <characteristic dimension=“y”>Brightness</characteristic> <characteristic dimension=“z”>Beatedness</characteristic> </characteristics>  <sequence>  <set>   <valuedimension=“x”>0.0</value>   <value dimension=“y”>0.0</value>   <valuedimension=“z”>0.0</value>  </set>  <set>   <valuedimension=“x”>0.0154</value>   <value dimension=“y”>0.0124</value>  <value dimension=“z”>0.0546</value>  </set>  <set>   <valuedimension=“x”>0.021</value>   <value dimension=“y”>0.123</value>  <value dimension=“z”>0.084</value>  </set>  <set>   <valuedimension=“x”>−0.05</value>   <value dimension=“y”>0.45</value>   <valuedimension=“z”>0.033</value>  </set>  <set>   <valuedimension=“x”>−0.235</value>   <value dimension=“y”>0.45</value>  <value dimension=“z”>0.121</value>  </set>  <set>   <valuedimension=“x”>−0.152</value>   <value dimension=“y”>0.332</value>  <value dimension=“z”>0.242</value>  </set>  <set>   <valuedimension=“x”>0.35</value>   <value dimension=“y”>0.133</value>   <valuedimension=“z”>0.343</value>  </set>  <set>   <valuedimension=“x”>0.1222</value>   <value dimension=“y”>−0.1541</value>  <value dimension=“z”>0.021</value>  </set>  </sequence></moodgradient>Document 3) Example of Playlist Generated from Mood Gradient

<?xml version=“1.0” encoding=“UTF-8”?> <playlist version=“1”xmlns=“http://xspf.org/ns/0/”xmlns:fs=“http://research.yahoo.com/featurespace/0”> <title>scissorkick.com and aurgasm.us after application of moodgradient</title>  <creator>William White</creator> <date>2007-08-26T09:43:12Z</date>  <fs:x>Tempo</fs:x> <fs:y>Brightness</fs:y>  <fs:z>Beatedness</fs:z> <fs:size>Ratings</fs:size>  <trackList>  <track>  <location>http://aurgasm.us/music/julija/Petra%20Jean%20Phillipson%20-%20Play%20Play.mp3</location>   <title>Petra Jean Phillipson - PlayPlay</title>  <image>http://images.amazon.com/images/P/23123423.jpg</image>  <fs:point x=“0” y=“0” z=“0” size=“0.33” />  </track>  <track>  <location>http://aurgasm.us/music/Priscilla%20Ahn%20-%20Lullaby.mp3</location>  <title>Priscilla Ahn - Lullaby</title>  <image>http://images.amazon.com/images/P/B0002Y0HXY.01._SCTHUMBZZZ_.jpg</image>  <fs:point x=“0.015” y=“011” z=“0.04” size=“0.93” />  </track>  <track>  <location>http://www.scissorkick.com/blog/music/01%20Sneaky%20Red.mp3</location>  <title>01 Sneaky Red</title>  <image>http://www.scissorkick.com/wordpress/wp-content/plugins/podpress/images/sneakyred.jpg</image>   <fs:pointx=“0.02” y=“0.1” z=“0.1” size=“0.5” />  </track>  <track>  <location>http://www.scissorkick.com/blog/music/cinematic_asthestarsfall.mp3</location>  <title>cinematic as the stars fall</title>  <image>http://www.scissorkick.com/wordpress/wp-content/plugins/podpress/images/audio_mp3_button.png</image>   <fs:pointx=“−0.02” y=“0.4” z=“0.03” size=“0.32” />  </track>  <track>  <location>http://aurgasm.us/music/julija/Tony%20Allen%20-%20Every%20Season.mp3</location>  <title>Tony Allen - Every Season (feat</title>  <image>http://ec1.images-amazon.com/images/P/432948023342_.jpg</image>  <fs:point x=“−0.25” y=“0.42” z=“0.23” size=“0.64” />  </track> <track>  <location>http://aurgasm.us/music/Marsen%20Jules%20-%20Coeur%20Saignant.mp3</location>  <title>Marsen Jules - Coeur Saignant</title>  <image>http://ec1.images-amazon.com/images/P/4329480230232_.jpg</image>  <fs:point x=“−0.15” y=“0.32” z=“0.24” size=“0.63” />  </track> <track>  <location>http://aurgasm.us/music/julija/Federico%20Aubele%20-%20Ante%20Tus%20Ojos.mp3</location>   <title>Federico Aubele - Ante TusOjos</title>  <image>http://ec1.images-amazon.com/images/P/43294802304_.jpg</image>  <fs:point x=“0.25” y=“0.22” z=“0.33” size=“0.44” />  </track>  <track>  <location>http://www.scissorkick.com/blog/music/02%20Clocks.mp3</location>  <title>02 Clocks</title>  <image>http://ec1.images-amazon.com/images/P/43294802304_.jpg</image>  <fs:point x=“0.15” y=“−0.02” z=“0.13” size=“0.55” />  </track> </trackList> </playlist>

Note the example of the generated playlist also has the additionalattribute “size” which, in the embodiment shown, corresponds to the userratings of the song. The size parameter is not used in similaritycalculation but can be used to convey additional information to theuser, along with the color of the shape in the three-dimensional space.All of the values for x, y, z, and size are normalized and will return anumber between −1.0 and 1.0 with the seed song assigned as the centroid.In the above example x denotes tempo, y denotes brightness, z denotesbeatedness and size represents the number of ratings the media objecthas received. These associations can be changed so that x, y, z canrepresent any acoustic feature and size can represent any piece ofnumeric metadata information available in the media object database.Because the playlist embodiment shown above includes the characteristics(i.e., the x, y, z values) for each song in the playlist, the system hasthe information necessary to easily map each song to a location inthree-dimensional x, y, z space and need not go through an additionalstep of retrieving characteristics for the songs in the playlist.

FIG. 6 illustrates an embodiment of a method for generating a playlistbased on a mood gradient. As discussed previously, a mood gradientrefers to a sequence of song characteristics. The mood gradient is usedas a template from which songs are selected. While an existing playlistmay be used to create a mood gradient, for example by determining andstoring the characteristics of the songs in the playlist as a moodgradient, a playlist is not a mood gradient. Alternatively, a list ofspecified characteristics, in which the characteristics do notcorrespond to any actual songs, in sequence is also a mood gradient. Agraphical representation of one characteristic of items in a moodgradient is illustrated in FIG. 7.

FIG. 7 illustrates a graphical representation of the values for onecharacteristic of items in a mood gradient, for example tempo in beatsper minute. The mood gradient is divided into 18 items. The value of thecharacteristic is illustrated by the height of a bar, with each barcorresponding to a different item/listing in the mood gradient. Intracing the height of the bar through the songs, one comes up with agradient curve over the course of the 18 songs for the characteristic.For example, FIG. 7 could be the characteristic of tempo in which eachbar represents a different song and the height represents a tempo value.Thus, as illustrated by the mood gradient of FIG. 7, the tempo slowlyincreases until reaching a maximum at about the 11^(th) or 12^(th) song.Then the tempo trails off. Depending on the number of characteristicsselected for the individual items in the mood gradient, multiplegradient curves can be generated to illustrate the various changes ineach characteristic.

While FIG. 7 illustrates one-dimension (i.e., one characteristic) of amood gradient in both bar graph and gradient curve, the path 50 of FIG.4B may be considered to illustrate three-dimensions of a mood gradientas a curve through the three-dimensional space. This illustrates oneaspect of a mood gradient: that the mood gradient may be considered alist of points or a defined path through multi-dimensional spacecorresponding to the objective characteristic data of the songs known tothe system.

Returning now to FIG. 6, in the method 600, a user requests a playlistbe created based on a mood gradient. This request is received in areceive request operation 602. The request may identify a pre-existingmood gradient or may provide the data of the mood gradient, e.g., thecharacteristic values of songs comprising individual items of the moodgradient. The mood gradient identified may be one that was previouslycreated by the user issuing the request or by one generated by someother user to which the requester or the system has access. In theembodiment shown, the mood gradient is a set of at least threecharacteristics (e.g., values associated with a characteristicidentifier) in a sequence and the user is requesting a new playlist bedynamically generated in which each song in the new playlist is similarin characteristics to the characteristics in the mood gradient. In anembodiment, the mood gradient is provided with the request. In analternative embodiment, the request identifies a predetermined moodgradient known to the system or information that allows the system,based on its knowledge of the user, to identify a mood gradient.

After receiving the request, the mood gradient is located and retrievedin a retrieve operation 604. The mood gradient may be retrieved from therequest itself or from a storage location. In an embodiment, furtherquerying may need to be performed as part of the retrieve operation 604.For example, the user may have specified only a category or a type ofmood gradient, e.g., “I am feeling mellow today”, and further selectionmay be necessary to identify and obtain an actual mood gradient.Ultimately, however, the mood gradient is retrieved in the retrieveoperation 604. In the embodiment, the mood gradient includes a sequenceof value sets, each value set containing a group of values, in whicheach value (or subset of values for characteristics with complexmathematical representations) describes a different objectivecharacteristic of a song.

After the mood gradient has been retrieved, for each value set in thesequence of the mood gradient a similar or matching song is identifiedin an identify similar song operation 606. This operation may be doneusing any known similarity matching algorithm. For example, all songswithin some threshold, e.g., ±5%, of the target values in the moodgradient for each characteristic may be identified and then a randomselection made from the set of identified songs. As another example, asimple weighting algorithm may be used to find a song similar to a givenvalue set. For instance, given a target value set having values (x₁, y₁,z₁) for three characteristics, all songs known in to the system may beranked based on the following algorithm:Ranking for song n=α(x ₁ −x _(n))²+β(y ₁ −y _(n))²+γ(z ₁ −z _(n))²

In which algorithm, the factors α, β, and γ are weighting factorspredetermined by the system administrator. Other algorithms and methodsof selecting songs similar to a target set of values are known in theart and any suitable method may be used in the identify similar songoperation 606.

The set of identified songs may also be filtered based on other factorssuch as user consumption history (so as not to repeatedly select songsthat have been recently rendered) and user preferences (so as not toplay songs the user has rated as disliked by the user). Any suitablemethod for selecting songs based on a comparison of that song'scharacteristics with a seed set of characteristics may be used in theidentify similar song operation 606.

After a similar song for each value in the mood gradient set has beenselected, a playlist is generated in a generate playlist operation 608.The playlist contains each of the selected songs in its appropriateposition in the sequence of the mood gradient. The playlist thusgenerated may be in any form, including those described above withreference to FIG. 5.

After the playlist has been generated, it is then transmitted to theuser's device in a transmission operation 610. In a client-serverembodiment, this operation 610 may include determining what playlistform is appropriate for the client device and reformatting the music orthe playlist to meet the device requirements. In a local embodiment,transmission may be a simple matter of placing the list of songs themedia player queue for playback in the appropriate order.

Depending on the nature of the original request, what is transmitted tothe user may be the songs identified in the identification operation606, transmitted or streamed to the rendering device of the user in thesequence of the mood gradient. Alternatively, the playlist generatedcould be a playlist in some known playlist format such as an RSS or Atomfeed and that feed file transmitted to the rendering device. Uponselection by the rendering device, the feed file is then accessed as isknown in the art in order to retrieve the songs in the sequence andrender them to the user whenever the user so desires.

The method 600 may be used by a user in order to customize the user'saudio experience. For example, as discussed above, a user may have aspecific mood gradient for use when the user is exercising. The moodgradient may start off with certain tempo, brightness and beatednesscharacteristics for songs and then, at a predetermined point, the tempomay be increased in order to assist the user in increasing the user'slevel of effort during the exercise routine. Alternatively, a moodgradient may be designed for a work environment that, for example,during the course of a work day, may begin with music having certaincharacteristics and at predetermined points in the mood gradient changedto music having other characteristics reflecting the nature of the workday expected to be experienced by the user.

Thus, a user may create multiple mood gradients over time or obtain themfrom other sources. Each time the user requests a playlist based on oneof the mood gradient, the user will receive songs having the samecharacteristics at the same points in the sequence. However, the songsthemselves will differ. In an embodiment, a user may even be able torequest most songs to be played twice or selected twice within apredetermined or selected period of time. For example, a user may usethe same mood gradient every day for a month with a particular songheard no more than once per week.

The method of FIG. 6 may also be used to generate a different set ofsongs from a pre-existing playlist. For example, a user may issue arequest to the system for a pre-existing playlist to be used as a moodgradient and to generate a new playlist based on the pre-existingplaylist. In response, the system may search the listing of songs in theplaylist and identify the appropriate value sets for each song. Thisinformation could then be passed to a similarity selection module with arequest to select songs based on the information. The songs are selectedand then the generated playlist transferred back to the media playerapplication for rendering.

FIG. 8 illustrates yet another embodiment of a method of selecting songsbased on a mood gradient and displaying the playlist to the user in themedia discovery interface. In the embodiment shown, the user issues arequest to play a predetermined mood gradient. The request identifiesthe mood gradient and may or may not contain the value sets for each ofthe songs in the sequence of the mood gradient.

The request is received in a receive request operation 802. If theinformation necessary to select the songs is not within the request, aretrieve requested mood gradient operation 804 is performed as describedabove. Next, a playlist of songs based on the mood gradient is generatedin a generation operation 806 as described above.

After the playlist has been generated, a three-dimensional space isgenerated in a generate interface operation 808. Next, the playlist isdisplayed as a path through the three-dimensional graphical space in adisplay playlist operation 810. The path may connect the icons in thespace that represent the songs of the playlist. Alternatively, the pathmay connect points corresponding to the various items (sets ofcharacteristic values) of the mood gradient. This allows the user tovisualize the changes in the characteristics of the songs that willoccur as the songs are rendered.

The display playlist operation 810 includes populating thethree-dimensional graphical space of the interface with at least thesongs selected for the playlist in the generate playlist operation 806,Other songs may also be included as being displayed as icons in thethree-dimensional graphical space. For example, songs not within theplaylist but that are close to the songs in the playlist in thethree-dimensional space may also be displayed.

The songs of the playlist are then played in their sequence to the userin a play songs operation 812. As the songs are played, each icon may bechanged, highlighted, rotated, spun or otherwise emphasized as itscorresponding song is being played to the user. The view shown by thethree-dimensional space may also be adjusted as different songs arerendered. For example, the relative view of three-dimensional space maybe adjusted as each song is rendered so that each songs currently beingrendered is the center of the view.

During the play operation 812, if a user issues a skip command, themethod will cease rendering the current song in the playlist and skip tothe next song in the playlist, which will have been selected as similarto the item in the mood gradient. This is one difference between thegenerating a playlist from a mood gradient and generating an entireplaylist from a single seed song (wherein a skip command results inanother song similar to the same seed set of characteristics, such asthe next most similar song).

The user may also be given the ability to change songs at differentpoints in the playlist by dragging the path so that one icon is nolonger in the path and another icon is. In this way, the user may“tweak” a playlist generated from a mood gradient to include songs thatthe user particularly wants to hear and which are still similar or veryclose to the characteristics of the mood gradient.

FIG. 9 illustrates an embodiment of a method for creating a moodgradient. In the method 900, a user issues a request to create a moodgradient that is received in a receive request operation 902. Either therequest contains a selection of songs or the user is, in response to thereceiving operation, prompted to make a selection of songs in order tocreate the mood gradient. The user's selection of songs is then receivedin a receive song selection operation 904. The selection of songs mayinclude an identification of each song in the mood gradient and itslocation within the sequence making up the mood gradient.

The method 900 may also include a receive characteristics selectionoperation 905 in which the user selects a set of one or morecharacteristics for the system to store in the mood gradient. Asdiscussed above, any number of characteristics may be used in items inthe mood gradient. This allows the user maximum flexibility to select amood gradient based on any characteristic or set of characteristics thatthe system can determine from music data. In an alternative embodiment,the method 900 may include selecting a fixed set or a default set ofcharacteristics to use and the receive characteristics selectionoperation 905 may be omitted.

After the selection of songs has been received, a generate value setsoperation 906 is performed. The generate value sets operation 906 mayinclude analyzing each song using an analysis module as discussed aboveor may include retrieving value sets previously generated for each ofthe songs selected for the mood gradient. In yet another embodiment, acombination of both retrieval and generation may be performed,especially in a case in which a selected song is not known to the systemcreating the mood gradient.

After the value sets have been generated, the mood gradient is stored asa sequence of value sets in a storage operation 908. Storage may includestoring an identification of the user who created the mood gradient aswell as an identification of when the mood gradient was created and adescription of what the mood gradient is or is for.

The mood gradient may include both an identifier of the songs in themood gradient and the value sets for the songs. If so, the mood gradientmay then be published as a mood gradient and used either as a moodgradient or a playlist.

In the embodiment of the method 900 shown in FIG. 9 and discussed above,the user creates a mood gradient by selecting songs from which theindividual characteristic value sets are derived. In an alternativeembodiment not shown, the user may provide or directly select theindividual characteristic value sets. In such an alternative embodiment,the receive song selection operation 904, receive characteristicsselection operation 905 and generate value sets operation 906 arereplaced by a receive characteristics operation (not shown).

The systems and methods described herein may also be used for theordering of a set of songs or other media objects. The ordering of a setof media tracks can determine the quality of the media consumptionexperience. In the case of an existing playlist, audio features andcharacteristics may be used to find the smoothest path between all thetracks in the playlist and generate an optimal ordering. Ordering mayalso be used to filter the results of a search request, returning onlythose media objects which fit both the search and ordering requirements.

In an embodiment, a user may create a gradient curve for one or morecharacteristics, such as the curve 702 shown in FIG. 7. These curves maybe created by drawing a line through the three-dimensional space, or bysome other input from the user. For a given dataset, there may be manypossible songs selections which generally fit the curve the user hasproposed. Therefore, a user can define a specific curve they areinterested in—say perhaps the ordering of music they like to hear overthe course of an afternoon or a Saturday night—and have different mediaobjects fill in the results each day. We can also use compound featuressuch as “mood” to provide finer granularity and incorporate multiplefeatures such as tempo, brightness and beatedness—possibly allrestricted to a certain genre, artist or other piece of traditionalmetadata.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible. In addition, methods describedin the foregoing disclosure may be adjusted by performing the operationsin a different order. For example, some operations may be performedeither in anticipation of another operation or dynamically as neededwhile providing the end result as described.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure. For example, the media discoveryinterface could be provided with additional features that make itsuitable for an ongoing display during the rendering of media objects.As another example, as a song is being rendered, the interface coulddisplay information, including advertisements relative to the currentlyrendered song, that are relevant to the song being rendered. As anotherexample, the mood gradient could contain any number of characteristicsfor each item in the gradient allowing for finer granularity in theselection of the songs matching the gradient. As another example, themood gradient could be a simple starting point and ending point and theuser may request a playlist containing any number of songs that create arelatively direct path between the two identified points. The systemwould then return the appropriate length playlist with songs on a known“trajectory” between the two points.

Numerous other changes may be made that will readily suggest themselvesto those skilled in the art and which are encompassed in the spirit ofthe invention disclosed and as defined in the appended claims.

What is claimed is:
 1. A method comprising: receiving, at a mediarendering device, a request for a play list of songs in accordance witha mood of a user; retrieving, by the media rendering device and based onthe user mood, a defined mood gradient comprising an identifieridentifying a mood and a sequence of target value sets for theidentified mood, each target value set including a plurality of values,the plurality of values comprising at least three values, each value ineach target value set corresponding to a dimension of amulti-dimensional space in which at least one song is represented, themulti-dimensional space having at least three dimensions, each dimensionof the multi-dimensional space corresponding to a different one ofobjective musical characteristics comprising at least tempo, brightnessand beatedness, an objective analysis of audio data of the at least onesong being used to generate the at least three values for the at leastone song; for each target value set in the mood gradient, selecting, bythe media rendering device, a song based on the at least three values inthe target value set; and generating, by the media rendering device, theplaylist of songs for playback, the play list containing each selectedsong.
 2. The method of claim 1, wherein retrieving a defined moodgradient further comprises: identifying a user associated with therequest; and selecting the defined mood gradient based on the userassociated with the request.
 3. The method of claim 1, whereinretrieving a defined mood gradient further comprises: selecting thedefined mood gradient based on a mood gradient identifier contained inthe request.
 4. The method of claim 1, wherein selecting a song furthercomprises: generating, based on an objective analysis of audio data foreach of a plurality of songs, a song value set associated with eachsong, each song value set including the at least three values in whicheach value represents a different objective musical characteristic ofthe associated song; and for each target value set in the mood gradient,comparing the target value set in the mood gradient to each song valueset.
 5. The method of claim 4 further comprising: for each target valueset in the mood gradient, selecting the song having the song value setthat most closely matches the target value set.
 6. The method of claim 4further comprising: for each target value set in the mood gradient, theselecting a song further comprising: identifying a set of similar songshaving song value sets within a predetermined variation of the targetvalue set; and randomly selecting the song from the set of similarsongs.
 7. The method of claim 4 further comprising: for each targetvalue set in the mood gradient: identifying a set of similar songshaving song value sets within a predetermined variation of the targetvalue set; and filtering the set of similar songs based on one or morefilter criteria; and selecting the song from the filtered set of similarsongs.
 8. The method of claim 3 wherein the request for a playlist isreceived from a first user and the mood gradient was created by a seconduser different from the first user.
 9. A system comprising: an analysismodule that objectively analyzes audio data of at least one song and,for each song, generates a multi-dimensional value set having at leastthree dimensions, each value set including a value for each of at leastthree objective characteristics of the associated song, the at leastthree objective characteristics comprising at least tempo, brightnessand beatedness, each of the three objective characteristics beingrepresented by one of the three values and corresponding to a differentdimension of a multi-dimensional space in which the at least one song isrepresented, the multi-dimensional space having at least threedimensions; a datastore containing a plurality of value sets, each valueset associated with a different song; a user interface module thatreceives a user selection of a mood gradient in accordance with areceived mood of a user, the mood gradient comprising an identifier of amood and a group of selected value sets in a sequence for the identifiedmood, each selected value set in the group including at least threevalues, each value corresponding to a dimension of the multi-dimensionalspace; and a playlist generator module that selects a recommended songfor playback for each selected value set in the group included in themood gradient based on the at least three values of the selected valueset.
 10. The system of claim 9, wherein the playlist generator modulefurther generates a playlist including the recommended song selected foreach selected value set in the mood gradient.
 11. The system of claim 9,wherein the user interface module further generates a three-dimensionaluser interface in a display device connected to the system, the userinterface configured to represent a three-dimensional space including atleast one first icon representing a first song located in thethree-dimensional space based on the values of three of the objectivecharacteristics of the first song.
 12. The system of claim 9, whereinthe user interface module receives from the user a first selection ofsongs in a sequence and a request to generate a play list containing asecond selection of songs based on the first selection of songs.
 13. Thesystem of claim 12, wherein the first selection of songs contains afirst song and a second song in a predetermined sequence and the secondselection of songs contains a third song selection based on itssimilarity to the first song and a fourth song selected based on itssimilarity to the second song.
 14. The system of claim 13 wherein thethird song and fourth song are in the same sequence as the first songand the second song.
 15. The system of claim 9 further comprising: amedia player that renders songs in accordance with commands receivedfrom a user, wherein the user interface module provides an interface forthe media player through which the user may control the selection andrendering of songs to the user.
 16. A computer-readable non-transitorystorage medium storing computer executable instructions for a method,the method comprising: receiving a request for a play list of songs inaccordance with a mood of a user; retrieving, based on the user mood, adefined mood gradient comprising an identifier of a mood and a sequenceof target value sets for the identified mood, each target value setincluding a plurality of values, the plurality of values comprising atleast three values, each value in each target value set corresponding toa dimension of a multi-dimensional space in which at least one song isrepresented, the multi-dimensional space having at least threedimensions, each dimension of the multi-dimensional space correspondingto a different one of objective musical characteristics comprising atleast tempo, brightness and beatedness, an objective analysis of audiodata of the at least one song being used to generate the at least threevalues for the at least one song; for each target value set in the moodgradient, selecting a song based on the at least three values in thetarget value set; and generating a playlist of songs for playback, theplay list containing each selected song.
 17. The medium of claim 16,wherein retrieving a defined mood gradient further comprises:identifying a user associated with the request; and selecting thedefined mood gradient based on the user associated with the request. 18.The medium of claim 16, wherein retrieving a defined mood gradientfurther comprises: selecting the defined mood gradient based on a moodgradient identifier contained in the request.
 19. The medium of claim16, the method further comprising: generating, based on an objectiveanalysis of audio data for each of a plurality of songs, a song valueset associated with each song, each song value set including the atleast three values in which each value represents a different objectivemusical characteristic of the associated song; wherein, for each targetvalue set in the mood gradient, selecting a song further comprises:identifying a set of similar songs having song value sets within apredetermined variation of the target value set; and randomly selectingthe song from the set of similar songs.
 20. The medium of claim 16, themethod further comprising: generating, based on audio data for each of aplurality of songs, a song value set associated with each song, eachsong value set including a plurality of values in which each valuerepresents a different objective musical characteristic of theassociated song; wherein, for each target value set in the moodgradient, selecting a song further comprises: identifying a set ofsimilar songs having song value sets within a predetermined variation ofthe target value set; and filtering the set of similar songs based onone or more filter criteria; and selecting the song from the filteredset of similar songs.